How to train the network only on one output when there are multiple outputs?

Question

I am using a multiple output model in Keras

model1 = Model(input=x, output=[y2, y3])

model1.compile((optimizer=\'sgd\', loss=cutom_loss_function)


        

Answer 1

Sharapolas' answer is right.

However, there is a better way than using a layer for building custom loss functions with complex interdependence of several outputs of a model.

The method I know is being used in practice is by never calling model.compile but only model._make_predict_function(). From there on, you can go on and build a custom optimizer method by calling model.output in there. This will give you all outputs, [y2,y3] in your case. When doing your magic with it, get a keras.optimizer and use it's get_update method using your model.trainable_weights and your loss. Finally, return a keras.function with a list of the inputs required (in your case only model.input) and the updates you just got from the optimizer.get_update call. This function now replaces model.fit.

The above is often used in PolicyGradient algorithms, like A3C or PPO. Here is an example of what I tried to explain: https://github.com/Hyeokreal/Actor-Critic-Continuous-Keras/blob/master/a2c_continuous.py Look at build_model and critic_optimizer methods and read kreas.backend.function documentation to understand what happens.

I found this way to have frequently problems with the session management and does not appear to work in tf-2.0 keras at all currently. Hence, if anyone knows a method, please let me know. I came here looking for one :)

Answer 2

I only want to train the network on output y2.

Based on Keras functional API guide you can achieve that with

model1 = Model(input=x, output=[y2,y3])   
model1.compile(optimizer='sgd', loss=custom_loss_function,
                  loss_weights=[1., 0.0])

What is the shape/structure of the y_pred and y_true argument in loss function when multiple outputs are used? Can I access them as above? Is it y_pred[0] or y_pred[:,0]

In keras multi-output models loss function is applied for each output separately. In pseudo-code:

loss = sum( [ loss_function( output_true, output_pred ) for ( output_true, output_pred ) in zip( outputs_data, outputs_model ) ] )

The functionality to do loss function on multiple outputs seems unavailable to me. One probably could achieve that by incorporating the loss function as a layer of the network.